Recent years have seen increasing use of convolutional neural networks to analyze overhead imagery collected by aerial vehicles or space-based sensors, for applications ranging from agriculture [1]} to surveillance [2]}, [3]} to land type classification [4]}. Segmentation and object detection of overhead imagery data requires identifying small, visually heterogeneous objects (e.g. cars and buildings) with varying orientation and density in images, a task ill-addressed by existing models developed for identification of comparatively larger and lower-abundance objects in natural scene images. The density and visual appearance of target objects change dramatically as look angle, geographic location, time of day, and seasonality vary, further complicating the problem. Addressing these challenges will provide broadly useful insights for the computer vision community as a whole: for example, how to build segmentation models to identify low-information objects in dense contexts.
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